Relevant bibliographies by topics / Time-aware metrics

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Time-aware metrics'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Time-aware metrics"

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López-Vizcaíno, Manuel, Laura Vigoya, Fidel Cacheda, and Francisco J. Novoa. "Time-Aware Detection Systems." Proceedings 21, no. 1 (2019): 39. http://dx.doi.org/10.3390/proceedings2019021039.

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Communication network data has been growing in the last decades and with the generalisation of the Internet of Things (IoT) its growth has increased. The number of attacks to this kind of infrastructures have also increased due to the relevance they are gaining. As a result, it is vital to guarantee an adequate level of security and to detect threats as soon as possible. Classical methods emphasise in detection but not taking into account the number of records needed to successfully identify an attack. To achieve this, time-aware techniques both for detection and measure may be used. In this w
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Koksal, C. E., and H. Balakrishnan. "Quality-Aware Routing Metrics for Time-Varying Wireless Mesh Networks." IEEE Journal on Selected Areas in Communications 24, no. 11 (2006): 1984–94. http://dx.doi.org/10.1109/jsac.2006.881637.

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MARY, S. A. SAHAAYA ARUL, and R. KRISHNAMOORTHI. "TIME-AWARE AND WEIGHTED FAULT SEVERITY BASED METRICS FOR TEST CASE PRIORITIZATION." International Journal of Software Engineering and Knowledge Engineering 21, no. 01 (2011): 129–42. http://dx.doi.org/10.1142/s0218194011005116.

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Test metrics succeed in analyzing the current level of maturity in testing and give a projection on the way to proceed with testing activities by allowing us to set goals and predict future trends. The objective of test metrics is to capture the planned and actual quantities: the effort, time and resources required to complete all the phases of development of the software project. Test case prioritization is an effective and practical technique in regression testing. It schedules test cases in order of precedence that increases their ability to meet some performance goals, such as code coverag
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Choi, Janghoon. "Target-Aware Feature Bottleneck for Real-Time Visual Tracking." Applied Sciences 13, no. 18 (2023): 10198. http://dx.doi.org/10.3390/app131810198.

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Recent Siamese network-based visual tracking approaches have achieved high performance metrics on numerous recent visual tracking benchmarks, where most of these trackers employ a backbone feature extractor network with a prediction head network for classification and regression tasks. However, there has been a constant trend of employing a larger and complex backbone network and prediction head networks for improved performance, where increased computational load can slow down the overall speed of the tracking algorithm. To address the aforementioned issues, we propose a novel target-aware fe
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López-Vizcaíno, Manuel F., Francisco Novoa, Diego Fernandez, and Fidel Cacheda. "Time Aware F-Score for Cybersecurity Early Detection Evaluation." Applied Sciences 14, no. 2 (2024): 574. https://doi.org/10.3390/app14020574.

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With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These resu
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Kim, Ga-Yeong, Su-Min Lim, and Ieck-Chae Euom. "A Study on Performance Metrics for Anomaly Detection Based on Industrial Control System Operation Data." Electronics 11, no. 8 (2022): 1213. http://dx.doi.org/10.3390/electronics11081213.

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Recently, OT (operational technology) networks of industrial control systems have been combined with IT networks. Therefore, OT networks have inherited the vulnerabilities and attack paths existing in IT networks. Consequently, attacks on industrial control systems are increasing, and research on technologies combined with artificial intelligence for detecting attacks is active. Current research focuses on detecting attacks and improving the detection accuracy. Few studies exist on metrics that interpret anomaly detection results. Different analysis metrics are required depending on the charac
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Rodríguez-Pupo, Luis, Carlos Granell, and Sven Casteleyn. "An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 54. http://dx.doi.org/10.3390/ijgi8020054.

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In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture mean
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Kim, Bosung, Gyu-min Lee, and Byeong-hee Roh. "MAC Protocol for Quality-Aware Real-Time Voice Delivery in Cognitive Radio-Enabled WSNs." International Journal of Distributed Sensor Networks 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/952951.

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We propose a quality-aware media access control (MAC) protocol for real-time voice delivery in cognitive radio- (CR-) enabled wireless sensor networks (WSNs). The temporal structure of the system model is addressed by using periodic timeslots in order to make more efficient use of the spectrum. In our proposed temporal structure, a bandwidth broker in such centralized CR networks (CCRNs) is selected as a central counterpart to synchronize with secondary users (SUs) and assign spectral resources to them. We develop an analytical model for SUs for the Call Admission Control (CAC) of voice traffi
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Hossain, Georges, Rondeau, and Divoux. "Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing?" Sensors 19, no. 13 (2019): 2901. http://dx.doi.org/10.3390/s19132901.

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There are all sort of indications that Internet usage will go only upwards, resulting in an increase in energy consumption and CO2 emissions. At the same time, a significant amount of this carbon footprint corresponds to the information and communication technologies (ICT) sector, with around one third being due to networking. In this paper we have approached the problem of green networking from the point of view of sustainability. Here, alongside energy-aware routing, we have also introduced pollution-aware routing with environmental metrics like carbon emission factor and non-renewable energ
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Rotar, Raul, Sorin Liviu Jurj, Flavius Opritoiu, and Mircea Vladutiu. "Fault Coverage-Aware Metrics for Evaluating the Reliability Factor of Solar Tracking Systems." Energies 14, no. 4 (2021): 1074. http://dx.doi.org/10.3390/en14041074.

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This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware m
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Dissertations / Theses on the topic "Time-aware metrics"

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Choudhury, Nazim Ahmed. "Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18640.

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Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynami
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Book chapters on the topic "Time-aware metrics"

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Sánchez, Pablo, and Alejandro Bellogín. "Time-Aware Novelty Metrics for Recommender Systems." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76941-7_27.

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Seiffarth, Johannes, Luisa Blöbaum, Richard D. Paul, et al. "Tracking One-in-a-Million: Large-Scale Benchmark for Microbial Single-Cell Tracking with Experiment-Aware Robustness Metrics." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91721-9_20.

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Abstract Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking p
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Kumpu, Jonna, Juho Pesonen, and Johanna Heinonen. "Measuring the Value of Social Media Marketing from a Destination Marketing Organization Perspective." In Information and Communication Technologies in Tourism 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_35.

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AbstractEven though social media is one of the most significant marketing tools in tourism, the measurement of its value is still developing. Assessing return-on-investment on social media marketing is challenging. Thus, destination marketing organizations (DMOs) are nonetheless pouring money and time in social media marketing without being aware of the results. In this study, we seek to understand what DMOs are measuring in social media marketing that they do and why. The qualitative data was gathered via semi-structured interviews among eight representatives of Finnish DMOs. The interview re
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Bueno, Renato, Daniel S. Kaster, Agma Juci Machado Traina, and Caetano Traina. "Time-Aware Similarity Search: A Metric-Temporal Representation for Complex Data." In Advances in Spatial and Temporal Databases. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02982-0_20.

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Mina, Amir I., Jeremy U. Espino, Allison M. Bradley, Parthasarathy Thirumala, Kayhan Batmanghelich, and Shyam Visweswaran. "Time-Series Aware Metrics for the Evaluation of Intraoperative Electroencephalography-Based Ischemia Detection." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti230970.

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Continuous intraoperative monitoring with electroencephalo2 graphy (EEG) is commonly used to detect cerebral ischemia in high-risk surgical procedures such as carotid endarterectomy. Machine learning (ML) models that detect ischemia in real time can form the basis of automated intraoperative EEG monitoring. In this study, we describe and compare two time-series aware precision and recall metrics to the classical precision and recall metrics for evaluating the performance of ML models that detect ischemia. We trained six ML models to detect ischemia in intraoperative EEG and evaluated them with
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Benmeziane, Hadjer, Kaoutar El Maghraoui, Hamza Ouarnoughi, and Smail Niar. "Pareto Rank-Preserving Supernetwork for Hardware-Aware Neural Architecture Search." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230276.

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In neural architecture search (NAS), training every sampled architecture is very time-consuming and should be avoided. Weight-sharing is a promising solution to speed up the evaluation process. However, training the supernetwork incurs many discrepancies between the actual ranking and the predicted one. Additionally, efficient deep-learning engineering processes require incorporating realistic hardware-performance metrics into the NAS evaluation process, also known as hardware-aware NAS (HW-NAS). In HW-NAS, estimating task-specific performance and hardware efficiency are both required. This pa
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Kulkarni, Nandkumar Prabhakar, Neeli Rashmi Prasad, and Ramjee Prasad. "An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous WSNs." In Sensor Technology. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch011.

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Researchers have faced numerous challenges while designing WSNs and protocols in numerous applications. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious deliberation. To tackle these two problems, the authors have considered Mobile Wireless Sensor Networks (MWSNs). In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP selects the optimal path from source node to sink by means of various metrics such as Average Energy consumption, Control Overh
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Muralidhar, L. B., N. Sathyanarayana, H. R. Swapna, Sheetal V. Hukkeri, and P. H. Reshma Sultana. "Machine Learning Advancing Diversity Equity and Inclusion in Data-Driven HR Practices." In Advances in Human Resources Management and Organizational Development. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0149-5.ch019.

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This work explores the transformative potential of Machine Learning (ML) in advancing Diversity, Equity, and Inclusion (DEI) within data-driven Human Resource (HR) practices. ML addresses systemic inequities in recruitment, promotions, and workplace engagement through tools that identify biases, automate fairness-focused evaluations, and analyse inclusion metrics. While offering unparalleled efficiency and scalability, ML introduces algorithmic bias, ethical complexities, and regulatory compliance challenges. Organisations can foster transparency, trust, and accountability by integrating fairn
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Singhvi, Dimple, Pranjal Nikhade, and K. Nimala. "Enhancing Grammar Correction With Error Type Classification and Insights of Quantum NLP and Networking." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-9336-9.ch012.

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This chapter presents a novel grammatical error correction (GEC) system leveraging the powerful T5 transformer model. Unlike traditional GEC tools, this system goes beyond simply correcting errors. It integrates ERRANT, a specialized tool that classifies the specific type of error present (e.g., subject-verb agreement, verb tense inconsistencies). This combined approach gives users a deeper understanding of their grammatical mistakes, fostering improved language proficiency, where evaluation metrics confirm significant advancements in GEC performance. The research delves further by exploring t
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Vijaya, J., Suvankar Paul, and Rohan Sharma. "Impact of Artificial Intelligence and Machine Learning Techniques in Database Management System Components." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1210-1.ch003.

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The integration of artificial intelligence (AI) and machine learning (ML) in database management systems (DBMS) is revolutionizing how data is managed and analyzed. This review highlights the role of AI/ML in enhancing DBMS functions such as intelligent transaction scheduling (e.g., conflict prediction in OLTP), adaptive query optimization (e.g., reinforcement learning for plan selection), and automated configuration tuning (e.g., workload-aware systems like RocksDB). Key components include data preprocessing (feature extraction, normalization), architectural integration (embedding ML into opt
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Conference papers on the topic "Time-aware metrics"

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Spinnler, Bernhard, Juraj Slovak, Hao Su, Sharfuddin Syed, Ashwin Gumaste, and Harald Bock. "Self-Calibrating Transponder using Intelligent DSP Metrics for Efficient Optical Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ofc.2023.m3z.15.

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We demonstrate a self-aware automated transponder system for disaggregated open ROADM-based networks with automation using real-time inputs from a PS-capable 800Gb/s DSP that facilitates in situ measurement of signal performance, for optimal line-rate selection.
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Jin, Tao, Siyu Huang, Ming Chen, Yingming Li, and Zhongfei Zhang. "SBAT: Video Captioning with Sparse Boundary-Aware Transformer." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/88.

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In this paper, we focus on the problem of applying the transformer structure to video captioning effectively. The vanilla transformer is proposed for uni-modal language generation task such as machine translation. However, video captioning is a multimodal learning problem, and the video features have much redundancy between different time steps. Based on these concerns, we propose a novel method called sparse boundary-aware transformer (SBAT) to reduce the redundancy in video representation. SBAT employs boundary-aware pooling operation for scores from multihead attention and selects diverse f
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Zhang, Runzhong, Suchen Wang, Yueqi Duan, Yansong Tang, Yue Zhang, and Yap-Peng Tan. "HOI-aware Adaptive Network for Weakly-supervised Action Segmentation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/191.

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In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of each frame with the neighboring frames. However, this would result in ambiguity when estimating similar actions, such as pouring juice and pouring coffee. To address this, we aim to exploit temporally global but spatially local human-object interactions (HOI) as video-level prior knowledge for action segmentation. The long-term HOI sequence provides crucial contextual information to distinguish ambiguous actions, wh
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Shao, Minglai, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, and Qin Tian. "Supervised Algorithmic Fairness in Distribution Shifts: A Survey." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/909.

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Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can lead to unfair predictions, disproportionately affecting certain groups characterized by sensitive attributes, such as race and gender. In this su
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Jamil, Neha, Mohammad Rasheed Khan, Syed Hassan Ali Shah, and _. Zubair. "Pioneering the Future of Real-Time Monitoring and Interventions with Generative AI and Automation." In Offshore Technology Conference. OTC, 2025. https://doi.org/10.4043/35853-ms.

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Abstract The oil and gas industry are undergoing a profound transformation with the adoption of advanced technologies designed to enhance operational efficiency, reduce costs, and minimize downtime in drilling operations. Central to this shift is the creation of interventions through a unified platform, which integrates real-time data acquisition with a robust backend database. This platform ensures streamlined data management and provides a foundation for advanced analytics by consolidating and organizing intervention data across wells and fields. Building on this centralized data repository,
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Ma, Liye, He Wang, and Longcong Wang. "Application of Global Importance Measures in RISMC for Dynamic Risk Assessment." In 2024 31st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/icone31-136084.

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Abstract Risk-Informed Safety Margin Characterization (RISMC) integrates traditional deterministic safety analysis methods with probabilistic safety analysis approaches by employing stochastic sampling of uncertain parameters and dynamic branching simulations of nuclear power plants. It enables time-dependent, uncertainty-aware, and multi-level risk assessments. In the process of RISMC analysis, quantifying the impacts of uncertainty parameters, response events, and system components on nuclear power plant risk can support the implementation of risk analysis results in nuclear power plants. Ho
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Miao, Daniel, Tyler Liu, and Andrew Park. "Factorial!: A Google Chrome Extension to Analyze and Rate News Articles using Machine Learning." In 3rd International Conference on Advances in Computing & Information Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131504.

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The advent and spread of the internet has caused many users to favor the convenience and breadth of reporting that online news offers, whether it be from media companies or social platforms, which in turn has led to the monetization and corruption of said stories [4]. Large, company-owned news sites each try to appeal to only a few groups across the political spectrum, oftentimes sacrificing the indifference and integrity which serve as the tenets of honest journalism. We propose to aid in solving this problem a Chrome extension which serves to provide metrics, information, and line-by-line an
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Poludasu, S., A. Groh, M. Snijder van Wissenkerke, and J. Harrist. "Predicting Rig Engine Utilization Using Machine Learning to Drive Fleet-Wide Emissions Reductions Initiatives." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215143-ms.

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Abstract With the drilling industry's focus on sustainability and digitalization, there are several data-driven studies published in the recent years which aim to reduce the operational fuel consumption and greenhouse gas (GHG) emission footprint through use of efficient generator management and alternative fuel sources. The possible savings from these optimization solutions are often measured with respect to a baseline - a model representing expected performance for the observed load profile. Since rig power demand and crew behaviors can vary immensely, estimating this baseline accurately can
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Briggs, Shannon, Emily Conway, Clare Arrington, et al. "Novice and expert performance in a knowledge graph-driven assistive dialogue system." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006139.

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This paper describes a LLM-supported assistive dialog system and a proposed evaluation methodology for this system, developed for Air Force intelligence analysts in intelligence reporting scenarios. Part of this technology has previously been described in other work which developed the core technology of the assistive dialog function (Sanders, A). This paper expands the previous system by incorporating RAG with a recommendation system. This recommendation system is informed by knowledge graphs. These knowledge graphs describe analysts’ progress through information foraging and sensemaking proc
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Vasilescu, Cristian, Cristian Beceanu, and Ijaz Hussain. "COLLABORATIVE OBJECT RECOGNITION FOR PARKING MANAGEMENT." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-026.

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The number of vehicles is constantly increasing and one of the challenges is efficient parking management. For this case, object recognition is required to detect and recognize cars, pedestrians, bicycles and obstacles in an indoor car parking. Cars must be recognized in order to indicate to drivers where to park their car, providing a semantic web of things. Vehicle recognition including car registration number must be followed by tracking, i.e., tracking the car in all subsequent image frames even if the image is partially obturated. Pedestrians should also be recognized for avoiding acciden
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